The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent the work is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.
The following references [1]-[31] provide background information related to the disclosure, and are incorporated herein by reference in their entirety:
[1] Cranach K F., Lepenies W., and Ploog D., “About brows: Emotional and conversational signals,” Cambridge, London, 1979;
[2] Kandemir R., and Ozmen G., “Facial Expression Classification with Haar Features, Geometric Features And Cubic Bezier Curves.” IU-Journal of Electrical & Electronics Engineering, vol. 13, no. 2, pp. 1667-1673, 2013;
[3] Fasel B., and Luettin J., “Automatic facial expression analysis: a survey,”. Pattern Recognition, vol. 36, no. 1, pp. 259-275, 2003;
[4] Khalid F., and Lili N., “3D Face Recognition Using Multiple Features for Local Depth Information,” IJCSNS, vol. 9, no. 1, pp. 27-32, 2009;
[5] Boughrara H., Chen L., Ben Amar C., and Chtourou M., “Face recognition under varying facial expression based on Perceived Facial Images and local feature matching,” In Information Technology and e-Services (ICITeS), 2012 International Conference on, Sousse, pp. 1-6, 2012;
[6] Das D., “Human's Facial Parts Extraction to Recognize Facial Expression,” International journal on information theory, vol. 3, no. 3, pp. 65-72, 2014;
[7] Chen Y., Zhang S., and Zhao X., “Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding,” Information, vol. 5, no. 2, pp. 305-318, 2014;
[8] Drira H., Ben Amor B., Srivastava A., Daoudi M., and Slama R., “3D face recognition under expressions, occlusions, and pose variations,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 35, no. 9, pp. 2270-2283, 2013;
[9] Lei Y., Bennamoun M., and El-Sallam A., “An efficient 3D face recognition approach based on the fusion of novel local low-level features,” Pattern Recognition, vol. 46, no. 1, pp. 24-37, 2013;
[10] Filko D., and Martinovic G., “Emotion recognition system by a neural network based facial expression analysis,” Automatika—Journal for Control, Measurement, Electronics, Computing and Communications, vol. 54, no. 2, pp. 263-272, 2013;
[11] Michalewicz Z., Genetic algorithms+data structures=evolution programs, Springer-Verlag, Berlin Heidelberg, 2013;
[12] Alsmadi M., Omar K., Noah S., and Almarashdeh I., “A hybrid memetic algorithm with back-propagation classifier for fish classification based on robust features extraction from PLGF and shape measurements,” Information Technology Journal, vol. 10, no. 5, pp. 944-954, 2011;
[13] Derbel H., Jarboui B., Hanafi S., and Chabchoub H., “Genetic algorithm with iterated local search for solving a location-routing problem,” Expert Systems with Applications, vol. 39, no. 3, pp. 2865-2871, 2012;
[14] Dixit M., and Silakari S., “A Hybrid Approach of Face Recognition Using Bezier Curve,” International Journal of Advanced Science and Technology, vol.71, pp. 41-48, 2014;
[15] Nowosielski A., “Mechanisms for increasing the efficiency of holistic face recognition systems,” methods, vol. 1, no. 5, pp. 7-9, 2015;
[16] Berbar M A., “Novel colors correction approaches for natural scenes and skin detection techniques,” International Journal of Video & Image Processing and Network Security IJVIPNS-IJENS, vol. 11, no. 2, pp. 1-10, 2011;
[17] Yogarajah P., Condell J., Curran K., McKevitt P., and Cheddad A., “A dynamic threshold approach for skin tone detection in colour images,” International Journal of Biometrics, vol. 4, no. 1, pp. 38-55, 2011;
[18] Berbar M A., “Skin colour correction and faces detection techniques based on HSL and R colour components,” International Journal of Signal and Imaging Systems Engineering, vol. 7, no. 2, pp. 104-115, 2014;
[19] Chai D., and Ngan K N., “Face segmentation using skin-color map in videophone applications. Circuits and Systems for Video Technology,” IEEE Transactions on, vol. 9, no. 4, pp. 551-564, 1999;
[20] Burange M S., and Dhopte S V., “Neuro Fuzzy Model for Human Face Expression Recognition,” IOSR Journal of Computer Engineering, vol. 1, no. 2, pp. 1-6, 2012;
[21] Vijayarani S., and Priyatharsini S., “Comparative Analysis of Facial Image Feature Extraction Algorithms,” Journal of Multidisciplinary Engineering Science and Technology, vol. 2, no. 2, pp. 107-112, 2015;
[22] Radial Curve, MathWorld—A Wolfram Web Resource, http://mathworld.wolfram.com/RadialCurve.html;
[23] Catenary Radial Curve, MathWorld—A Wolfram Web Resource.[Online]. [Accessed 21/5/2015], http://mathworld.wolfram.com/CatenaryRadialCurve.html;
[24] Pal A., “Bezier curves and representation of reservoir extent maps,” In 10th Biennial International Conference & Exposition, pp. 1-5;
[25] Wallhoff, F. (2013). Facial Expressions and. Emotion Database.[Online] [Accessed 21/7/2015], http://cotesys.mmk.e-technik.tu-muenchen.de/isg/content/feed-database;
[26] Badawi. U A., and Alsmadi M., “A Hybrid. Memetic Algorithm (Genetic Algorithm and Great Deluge Local Search) With Back-Propagation Classifier for Fish Recognition,” IJCSI, vol. 10, no. 2, pp. 1694-0784, 2014;
[27] Alsmadi M., Omar K B., Noah S A., and Almarashdeh I., “Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network,” Journal of Computer Science, vol. 6, no. 10, pp. 1088-1094, 2010;
[28] Wallhoff F., Schuller B., Hawellek M., and Rigoll G., “Efficient recognition of authentic dynamic facial expressions on the feedtum database,” In Multimedia and Expo, 2006 IEEE International Conference on, Toronto, Ont, pp. 493-496, 2006;
[29] Tan K C., Lee T H., Khoo D., and Khor E F., “A multiobjective evolutionary algorithm toolbox for computer-aided multiobjective optimization,” Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 31, no. 4, pp. 537-556, 2001;
[30] Baker J E., “Reducing bias and inefficiency in the selection algorithm,” In Proceedings of the second international conference on genetic algorithms, Lawrence Erlbaum Associates, Inc Hillsdale, New Jersey, pp. 14-21, 1987; and
[31] Sheta A F., “Estimation of the COCOMO model parameters using genetic algorithms for NASA software projects,” Journal of Computer Science, vol. 2, no. 2, pp. 118-123, 2006.
Facial expression is an important mean for human beings to interact with the environment and other persons in the vicinity. Facial expression is related to many sources as seen in FIG. 1. Automatic machine recognition of human facial expression enables a computing system to perceive and understand intentions and emotions as communicated by a human being. Various approaches have been studied in the field of automatic facial expression.
A two dimensional (2D) facial expression recognition approach is described in the reference [5]. The approach is based on local feature matching and perceived facial images, and employs a new biological vision-based facial description which is called Perceived Facial Images (PFIs). The approach is originally used for three dimensional (3D) face recognition, and aims to give a graphical representation simulating human visual perception. The approach is robust in affining the facial expressions and lighting transformations.
An individual independent real-time facial expression recognition method is described in the reference [6]. Geometry of a face is extracted by an adapted active shape model (ASM). Every face geometry part is efficiently represented using Census Transformation histogram of features. The face expressions are classified using support vector machine classifier and exponential chi-square weighted merging kernel. The method is tested on JAFFE database, and the testing results show that this method can be applied on real-time facial expression experiments and produces good accuracy results.
An approach for facial expression recognition using non-negative least squares sparse coding is described by the reference [7]. This proposed approach is utilized to build a classifier for facial expressions. In order to test the performance of the proposed approach, the raw pixels and the local binary patterns are extracted to represent the facial features. They also used the JAFFE database for testing the proposed approach and the results show that the approach obtains better accuracy rates compared with other facial expression recognition methods.
A new geometric framework is described in the reference [8]. The framework can be used to analyze 3D faces, and is based on specific aims of matching, comparing and averaging of faces shapes. The radial curves arising from the nose tip are employed. The obtained curves are used to develop a Riemannian framework using elastic shape analysis for exploring full shapes of facial surfaces. An elastic Riemannian metric is used for measuring facial distortions. The framework is robust to many challenges such as large pose variations, hair, different and large facial expressions and missing parts. The framework shows promising results for both theoretical and empirical perspectives. The proposed framework enables the formal statistical inferences. Test results are obtained based on three noticeable databases: Bosphorus, FRGCv2 and GavabDB, and exceeds or matches other similar methodologies.
A novel approach for 3D face recognition is described in the reference [9]. The approach is based on some low-level geometric features that are collected and extracted from nose and eyes-forehead areas. These areas are less subjective to distortions that may be produced due to the facial expressions. The extracted features are used to compute a region-based histogram descriptor. The Support Vector Machine (SVM) is used as a classifier. The experimental results show the success of the feature-level fusion scheme compared with score-level fusion in obtaining higher verification rates.
Two dimension facial images with different expressions are analyzed in the reference [2]. The expressions include anger, sadness, surprise, fear, smile, neutral and disgust. The Viola-Jones algorithm is used for face detector. The AdaBoost algorithm is used to locate and determine the face location. In order to find and extract mouth and eyes regions, Haar filters as well as the ratios of facial geometric are used to decrease the mouth and eyes detection error. Cubic Bezier curves are used in recognizing and determining the facial expressions. The FEEDTUM database is used in the training and testing processes to validate the proposed methods. The experimental results show that the success rates of the proposed methods ranged between 60% and 97% .
A 2D recognition system for human expressions is proposed by the reference [10]. The proposed system works based on studying the important keys in the facial regions using principal component analysis (PCA) and neural networks (NNs). PCA is used to decrease elements number in the features vectors. The NNs are used because they are adapted to specific classification problems, and pel mit higher flexibility in the training process. The FEEDTUM database is used to train and test the proposed methods. The obtained results show high recognition accuracy range (46%-80%).
Genetic algorithm (GA) is a heuristic algorithm, and is a population based approach. GA simulates the process of selection in the nature, and is utilized to find solutions to complicated problems from a population of solutions. Generally, GA can include three major steps: selection, crossover, and mutation. A selection mechanism is used to select solutions for producing a next generation of population. Selection mechanisms can include Roulette Wheel Selection, Truncation Selection, and Tournament Selection. Crossover is performed in a mating procedure. The mating procedure finds new solutions (higher fitness value) from the search space. Mutation is a local search process that finds neighborhood solutions then disturbs the population to improve the search space. GA is described in the reference [11].
Iterated local search (ILS) is a simple, effective, robust, and easy to implement algorithm. ILS performs a local search in a partial sub-space as an alternative to the search in a whole solution search space. ILS aims to find a local optimum solution, then performs a solution perturbation and restarts a local search in a sub-space including the modified solution. ILS avoids being trapped in local optima by disturbing the local optimum solutions, and using them to generate new solutions randomly. An example of ILS algorithm is described in the reference [13].